DETAIL KOLEKSI

Model sistem business intelligence untuk mengukur kinerja publikasi ilmiah dosen


Oleh : Miwan Kurniawan Hidayat

Info Katalog

Penerbit : FTI - Usakti

Kota Terbit : Jakarta

Tahun Terbit : 2023

Pembimbing 1 : Deddy Sugiarto

Pembimbing 2 : Rina Fitriana

Subyek : Business intelligence

Kata Kunci : business intelligence, data analysis, data mining, K-Means, X-Means

Status Posting : Published

Status : Lengkap


File Repositori
No. Nama File Hal. Link
1. 2023_TS_MTI_163012100002_Halaman-Judul.pdf
2. 2023_TS_MTI_163012100002_Lembar-Pengesahan.pdf
3. 2023_TS_MTI_163012100002_Bab-1_Pendahuluan.pdf
4. 2023_TS_MTI_163012100002_Bab-2_Tinjauan-Pustaka.pdf
5. 2023_TS_MTI_163012100002_Bab-3_Metodologi-Penelitian.pdf
6. 2023_TS_MTI_163012100002_Bab-4_Analisis-dan-Pembahasan.pdf
7. 2023_TS_MTI_163012100002_Bab-5_Kesimpulan.pdf
8. 2023_TS_MTI_163012100002_Daftar-Pustaka.pdf
9. 2023_TS_MTI_163012100002_Lampiran.pdf

P Pengukuran kinerja publikasi ilmiah di universitas berdasarkan jumlah karya ilmiah yang dipublikasikan berperan dalam pengembangan dosen untuk meningkatkan mutu pendidikan. Data publikasi ilmiah bersumber dari situs web Sinta sebagai sistem informasi untuk mengukur kinerja peneliti, lembaga dan jurnal di Indonesia, namun akses dan analisis data untuk kebutuhan internal institusi terbatas serta informasi belum dimanfaatkan pada pola pengembangan dosen yang sesuai dengan karakteristik dosen. Tujuan penelitian yaitu analisis dan perancangan model sistem business intelligence untuk mengukur kinerja publikasi ilmiah menggunakan model dimensional, clustering, On-Line Analytical Processing (OLAP) dan pembuatan prototipe. Metode penelitian yang dilakukan melalui tahap analisis kebutuhan data dan informasi, perancangan data warehouse, penerapan data mining dan OLAP, pengembangan sistem business intelligence, dan evaluasi sistem. Penelitian ini menghasilkan model sistem business intelligence untuk mengukur kinerja publikasi ilmiah menggunakan model dimensional dengan menerapkan data mining dan OLAP. Model dimensional yang dihasilkan yaitu model indeks peneliti, model score peneliti, model artikel publikasi, model subjek penelitian. Pengukuran besar data dan waktu pemrosesan menunjukkan star schema memiliki data sebesar 336 KB dan waktu proses 0,00554 detik merupakan model terbaik dibandingkan snowflakes schema yang memiliki data sebesar 368 KB dan waktu proses 0,00611 detik. Pengukuran DBI menunjukkan bahwa kinerja clustering terbaik adalah algoritma X-Means dengan K sebanyak 5 cluster (Kmin=3, Kmax=5) serta nilai DBI sebesar 0,537040.

M Measurement of the performance of scientific publications at universities based on the number of published scientific papers plays a role in developing lecturers to improve the quality of education. Scientific publication data is sourced from the Sinta website to measure the performance of journals, institutions, and researchers in Indonesia. Still, access and analysis of data for internal institutional needs are limited, and information has not been utilized on lecturer development patterns that are by the characteristics of lecturers. The study aims to analyze and design business intelligence system models to measure the performance of scientific publications using dimensional models, clustering, On-Line Analytical Processing (OLAP), and prototyping. Research methods are carried out through data, and information needs analysis, data warehouse design, data mining and OLAP application, business intelligence system development, and system evaluation. This research produces a business intelligence system model to measure the performance of scientific publications using dimensional models by applying data mining and OLAP. The resulting dimensional models are the researcher index model, the researcher score model, the publication article model, and the research subject model. Measurements of data size and processing time show that the star schema has data of 336 KB and a processing time of 0.00554 seconds, is the best model compared to the snowflakes schema, which has data of 368 KB and a processing time of 0.00611 seconds. DBI measurements show the best clustering performance is the X-Means algorithm with K as many as 5 clusters (Kmin = 3, Kmax = 5) and a DBI value of 0.537040.

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